Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback-Reference-Cited by-同舟云学术

Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback

Published:2024-07-10 Issue: Volume:35 Page:48-58
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Dong Qian¹^ORCID,Liu Yiding²^ORCID,Ai Qingyao³^ORCID,Wu Zhijing⁴^ORCID,Li Haitao¹^ORCID,Liu Yiqun¹^ORCID,Wang Shuaiqiang⁵^ORCID,Yin Dawei⁵^ORCID,Ma Shaoping¹^ORCID

Affiliation:

1. DCST, Tsinghua University, Beijing, China

2. Baidu Inc., Beijing, Singapore

3. Quan Cheng Laboratory & DCST, Tsinghua University, Beijing, China

4. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

5. Baidu Inc., Beijing, China

Funder

Quan Cheng Laboratory

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657689

Reference69 articles.

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4. Mrinmoi Borah Pankaj Dadure Partha Pakray et al. 2022. Comparative analysis of T5 model for abstractive text summarization on different datasets. (2022).

5. Arthur Bravzinskas, Mirella Lapata, and Ivan Titov. 2019. Unsupervised opinion summarization as copycat-review generation. arXiv preprint arXiv:1911.02247 (2019).